ArticleMedical decision making : an international journal of the Society for Medical Decision Making2015
US-Based Drug Cost Parameter Estimation for Economic Evaluations.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Health care resource utilization and costs in patients with painful diabetic neuropathy treated with 10 kHz spinal cord stimulation therapy.Journal of managed care & specialty pharmacy · 2023Trial
- Cost Effectiveness of Dapagliflozin for Heart Failure Across the Spectrum of Ejection Fraction: An Economic Evaluation Based on Pooled, Individual Participant Data From the DELIVER and DAPA-HF Trials.Journal of the American Heart Association · 2024Article
- Estimating Drug Costs: How do Manufacturer Net Prices Compare with Other Common US Price References?PharmacoEconomics · 2018Article
- A Transparent and Consistent Approach to Assess US Outpatient Drug Costs for Use in Cost-Effectiveness Analyses.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2018Article
- Innovative assessment of inpatient and pulmonary drug costs for children with cystic fibrosis.Pediatric pulmonology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
introductionIn the United States, more than 10% of national health expenditures are for prescription drugs. Assessing drug costs in US economic evaluation studies is not consistent, as the true acquisition cost of a drug is not known by decision modelers. Current US practice focuses on identifying one reasonable drug cost and imposing some distributional assumption to assess uncertainty.
methodsWe propose a set of Rules based on current pharmacy practice that account for the heterogeneity of drug product costs. The set of products derived from our Rules, and their associated costs, form an empirical distribution that can be used for more realistic sensitivity analyses and create transparency in drug cost parameter computation. The Rules specify an algorithmic process to select clinically equivalent drug products that reduce pill burden, use an appropriate package size, and assume uniform weighting of substitutable products. Three diverse examples show derived empirical distributions and are compared with previously reported cost estimates.
resultsThe shapes of the empirical distributions among the 3 drugs differ dramatically, including multiple modes and different variation. Previously published estimates differed from the means of the empirical distributions. Published ranges for sensitivity analyses did not cover the ranges of the empirical distributions. In one example using lisinopril, the empirical mean cost of substitutable products was $444 (range = $23-$953) as compared with a published estimate of $305 (range = $51-$523).
conclusionsOur Rules create a simple and transparent approach to creating cost estimates of drug products and assessing their variability. The approach is easily modified to include a subset of, or different weighting for, substitutable products. The derived empirical distribution is easily incorporated into 1-way or probabilistic sensitivity analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.